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Intelligent image segmentation algorithm to optimize the Normalized Cut

Author: DiYanPeng
Tutor: GuoMin
School: Shaanxi Normal University
Course: Applied Computer Technology
Keywords: Image Segmentation Normalized divided Genetic Algorithms Particle swarm optimization
CLC: TP391.41
Type: Master's thesis
Year: 2011
Downloads: 106
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Abstract


Segmentation based on graph theory research hotspot in recent years, image segmentation, which the Normalized Cut a standardized form, it image pixels as nodes in the graph as the similarity between each pixel edge weight between nodes in the graph, the image map undirected weighted graph. But minimize Normalized Cut criteria is an NP-hard problem. Approximate solution to solving the NP-hard problem is the similarity matrix spectral decomposition can be obtained approximate optimal solution, ie eigenvectors Normalized Cut graph divided, and use it to guide the final completion of the image segmentation. This paper based on the theory Normalized Cut spectral clustering high computational complexity, the disadvantage of poor accuracy, mainly to do the work of the following three aspects: (1) The proposed dual-threshold image segmentation method based on the Normalized Cut criterion. Normalized Cut single thresholding methods in the single target grayscale image obtained good segmentation results, but it does not apply to multi-objective image segmentation, and their weights calculated without considering the pixel neighborhood information. This paper is the first to use the gray relational analysis of B-type correlation degree to evaluate the similarity between the pixels, this associate degree at the same time consider to be compared pixel and its neighborhood information. The then single threshold Normalized Cut Standards derivation for dual-threshold classification criteria, the final use of efficient particle swarm optimization algorithm to solve two segmentation threshold. This method is less time-consuming, high repeatability, multi-target image can be split. (2) gray image segmentation using a genetic algorithm to optimize the Normalized Cut guidelines. Guidelines for use of traditional methods minimize Normalized Cut spectral clustering algorithm, the algorithm can only be obtained by the approximate optimal solution guidance segmentation, and can not get a more accurate value. This paper is the first to use the fuzzy C-means clustering grayscale image preprocessing, by pre-treatment can reduce the size of the similarity matrix, reducing the complexity of the algorithm. Then use the parallel genetic algorithm instead of the spectral clustering algorithm for solving the minimum Normalized Cut. When the completion of the evolution of genetic algorithms, optimal chromosome instead of feature vectors to guide the division of the figure, and the resulting image and the segmentation results. The performance of the optimization algorithm is better than the traditional spectral clustering algorithm, so you can get more satisfactory grayscale image segmentation results. (3) true color image segmentation method using a binary discrete particle swarm optimization Normalized Cut criteria. True-color image color information overload cause it to split time consuming serious, this paper first uses fuzzy C-means clustering on the true-color image of R, G, B each channel is processed separately, and then take the intersection of the three results obtained computing, in order to obtain the image having a certain maximum similarity region to be segmented. Then use intelligent binary particle swarm algorithm instead of the spectral clustering meters minimize Normalized Cut criterion. When the particle swarm optimization iteration is complete, the optimal particle can replace feature vectors to guide the division of the figure, and ultimately get the true color image segmentation results. Optimization ability of the algorithm is superior to the spectral clustering algorithm. And can quickly get a true color image segmentation quality results.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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